Imaging Evaluation for Thoracic Spine Fractures in Pediatric Trauma Patients
Bibliographic record
Abstract
BACKGROUND: Imaging workup for evaluating thoracic spine fracture (TSF) in pediatric blunt trauma is variable. PURPOSE: The aim of the study was to determine the number of TSFs missed by radiography and identified on computed tomography (CT) or magnetic resonance imaging (MRI) that required intervention or resulted in a change in management. METHODS: A retrospective review of children with TSFs was performed. Diagnostic images and reports for these patients were reviewed. Data regarding demographics, clinical presentation, management, and outcomes were extracted from institutional electronic medical records. Use of radiographs, CT, and MRI for evaluation of TSF was quantified. Incidence of TSFs was calculated and stratified by mechanism. The number of TSFs and complicating factors missed on radiography but identified on subsequent CT or MRI were quantified. RESULTS: Three thousand two hundred sixty-five trauma patients 18 years or younger were reviewed. Of these, 3.3% (90/3265) had TSFs (36 females, 54 males; mean age, 10.80 ± 4.4 years). The most common mechanism of injury was fall (43% [39/90]) followed by motor vehicle collisions (30% [27/90]). The most common fracture was simple compression fracture 64%, which occurred most frequently in the mid thoracic spine, followed by transverse process fractures 19% and spinous process fractures 7%. Almost half of all TSFs diagnosed on CT and/or MRI were missed on initial radiographs. While all fractures that required operative management were identified on radiographs, 13 of the 19 fractures that required nonoperative intervention were missed. CONCLUSIONS: Approximately 50% of TSFs diagnosed on CT or MRI were not identified on preceding radiographs. This is similar to studies in adult populations that show poor sensitivity of radiographs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".